The modern communications era has brought about a tremendous expansion of wireline and wireless networks. Computer networks, television networks, and telephony networks are experiencing an unprecedented technological expansion, fueled by consumer demand. Wireless and mobile networking technologies have addressed related consumer demands, while providing more flexibility and immediacy of information transfer.
Current and future networking technologies continue to facilitate ease of information transfer and convenience to users. Due to the now ubiquitous nature of electronic communication devices, people of all ages and education levels are utilizing electronic devices to communicate with other individuals or contacts, receive services and/or share information, media and other content. One area in which there is a demand to increase ease of information transfer relates to three dimensional (3D) city modeling.
At present, reconstruction and display of 3D city models are important parts of next generation mapping systems, and 3D location and content services. Due to the number of constructions in an urban scene, automatic methods have been in high demand. Automatic computation of 3D city models has, in general, explored two different sources of data: (i) stereo photogrammetric imagery; and (ii) 3D sensors acquiring a 3D cloud of points. In this regard, the relative recent usage of 3D sensors for city survey (e.g., U.S. Geological Surveys (USGS)) has given the 3D city modeling field a new branch for research. The data collected by such sensors may be composed of a cloud of 3D points that are geo-referenced.
Automation of 3D city modeling may potentially reduce time and effort of specialized personnel. However, the broad academic research in the field indicates that the automatic generation of 3D city models may cause problems associated with extreme computational demand, as described below.
Current solutions to 3D modeling from a cloud of points typically employ parametric approaches utilizing a dictionary of predefined shapes that are optimized to fit on collected 3D data. For instance, shapes to be optimized may vary from low-level details such as lines and planes. At present, existing parametric approaches are limited to sensitive parameter setting and predefined shapes which typically require extreme computational demand. As such, at present, parametric approaches for reconstruction of 3D city models may be difficult to scale across cities with different architecture.
In view of the foregoing drawbacks, it may be desirable to provide an efficient and optimal mechanism for generating three dimensional geographical models.